Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe – modeling and observations

Abstract

Abstract. The Terrestrial Systems Modeling Platform (TSMP) was extended with a chemical transport model and polarimetric radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. The model was used at kilometer-scale (convection-permitting) resolution to simulate a deep convective storm event over Germany which produced large hail, high precipitation, and severe damaging winds. The ensemble model simulation was, in general, able to capture the storm structure, its evolution, and the spatial pattern of accumulated precipitation. However, the model was found to underestimate regions of high accumulated precipitation (> 35 mm) and convective area fraction in the early period of the storm. While the model tends to simulate too high reflectivity in the downdraft region of the storm above the melting layer (mostly contributed by graupel), the model also simulates very weak polarimetric signatures in this region, when compared to the radar observations. The above findings remained almost unchanged when using a narrower cloud drop size distribution (CDSD) acknowledging the missing feedback between aerosol physical and chemical properties and CDSD shape parameters. The kilometer-scale simulation showed that the strong updraft in the convective core produces aerosol-tower-like features, increasing the aerosol number concentrations and hence increasing the cloud droplet number concentration and reducing the mean cloud drop size. This could also be a source of discrepancy between the simulated polarimetric features like differential reflectivity (ZDR) and specific differential-phase (KDP) columns along the vicinity of the convective core compared to the X-band radar observations. However, the use of narrow CDSD did improve the simulation of ZDR columns. Besides, the evaluation of simulated trace gases and aerosols was encouraging; however, a low bias was observed for aerosol optical depth (AOD), which could be partly linked to an underestimation of dust mass in the forcing data associated with a Saharan dust event. This study illustrates the importance and the additional complexity associated with the inclusion of chemistry transport model when studying aerosol–cloud–precipitation interactions. But, along with polarimetric radar data for model evaluation, it allows us to identify and better constrain the traditional two-moment bulk cloud microphysical schemes used in the numerical weather prediction models for weather and climate.